Alexis Blandin, Farida Saïd, Jeanne Villaneau, P. Marteau
{"title":"Graphical document representation for french newsletters analysis","authors":"Alexis Blandin, Farida Saïd, Jeanne Villaneau, P. Marteau","doi":"10.1145/3558100.3563856","DOIUrl":null,"url":null,"abstract":"Document analysis is essential in many industrial applications. However, engineering natural language resources to represent entire documents is still challenging. Besides, available resources in French are scarce and do not cover all possible tasks, especially in specific business applications. In this context, we present a French newsletter dataset and its use to predict the good or bad impact of newsletters on readers. We propose a new representation of newsletters in the form of graphs that consider the newsletters' layout. We evaluate the relevance of the proposed representation to predict a newsletter's performance in terms of open and click rates using graph analysis methods.","PeriodicalId":146244,"journal":{"name":"Proceedings of the 22nd ACM Symposium on Document Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 22nd ACM Symposium on Document Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3558100.3563856","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Document analysis is essential in many industrial applications. However, engineering natural language resources to represent entire documents is still challenging. Besides, available resources in French are scarce and do not cover all possible tasks, especially in specific business applications. In this context, we present a French newsletter dataset and its use to predict the good or bad impact of newsletters on readers. We propose a new representation of newsletters in the form of graphs that consider the newsletters' layout. We evaluate the relevance of the proposed representation to predict a newsletter's performance in terms of open and click rates using graph analysis methods.
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